code: def train():
      model.train()
  
      total_loss = total_examples = 0
      for batch in tqdm.tqdm(train_loader):
          batch = batch.to(device)
          optimizer.zero_grad()
  
          pred = model(
              batch.x_dict,
              batch.edge_index_dict,
              batch["user", "item"].edge_label_index,
          )
          loss = F.binary_cross_entropy_with_logits(
              pred, batch["user", "item"].edge_label
          )
  
          loss.backward()
          optimizer.step()
          total_loss += float(loss)
          total_examples += pred.numel()
  
      return total_loss / total_examples
  
imports: import os.path
  import torch
  import torch.nn.functional
  import tqdm
  from sklearn.metrics import roc_auc_score
  from torch.nn import Embedding, Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import Taobao
  from torch_geometric.loader import LinkNeighborLoader
  from torch_geometric.nn import SAGEConv
  from torch_geometric.utils.convert import to_scipy_sparse_matrix
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown